Increase data length for seasonal model examples to meet minimum requirements

STL and LSD models require sufficient seasonal cycles in the data.
Increased from 60 to 120 data points (5 to 10 years of monthly data)
to ensure models have enough cycles to fit properly.

Changes:
- STLModel examples: 72 → 120 points
- LSDModel examples: 60 → 120 points
- Updated plot ranges to match new data lengths

This fixes the error: "Time series needs at least `ns` seasonal cycles"

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

Author: sbfnk

vignettes/forecast-models.Rmd

@@ -301,9 +301,9 @@ historical periods.
 ```{r lsd}
 # Monthly data with seasonality
 set.seed(123)
-months <- 1:60
+months <- 1:120
 seasonal_pattern <- 20 + 10 * sin(2 * pi * months / 12)
-data <- seasonal_pattern + rnorm(60, sd = 2)
+data <- seasonal_pattern + rnorm(120, sd = 2)
 
 # Fit LSD model
 model <- LSDModel(window_width = 3, s = 12) # Average last 3 years
@@ -314,10 +314,10 @@ fc <- forecast(fitted, interval_method = NoInterval(), horizon = 1:12)
 
 # Plot
 plot(data,
-  type = "l", xlim = c(1, 72),
+  type = "l", xlim = c(1, 132),
   main = "LSD Model: Seasonal Forecast"
 )
-lines(61:72, fc$mean, col = "red", lwd = 2)
+lines(121:132, fc$mean, col = "red", lwd = 2)
 ```
 
 **Parameter Guidance:**
@@ -531,10 +531,10 @@ where $T_t$ is trend, $S_t$ is seasonal, $R_t$ is remainder.
 ```{r stl}
 # Generate trend + seasonality
 set.seed(123)
-time <- 1:72 # 6 years of monthly data
+time <- 1:120 # 10 years of monthly data
 trend <- 0.5 * time
 seasonal <- 10 * sin(2 * pi * time / 12)
-data <- 50 + trend + seasonal + rnorm(72, sd = 2)
+data <- 50 + trend + seasonal + rnorm(120, sd = 2)
 
 # Fit STL model
 model <- STLModel(s = 12)
@@ -545,11 +545,11 @@ fc <- forecast(fitted, interval_method = NoInterval(), horizon = 1:12)
 
 # Plot
 plot(data,
-  type = "l", xlim = c(1, 84),
+  type = "l", xlim = c(1, 132),
   main = "STL Model: Trend + Seasonality",
   ylab = "Value"
 )
-lines(73:84, fc$mean, col = "red", lwd = 2)
+lines(121:132, fc$mean, col = "red", lwd = 2)
 ```
 
 **Parameter Guidance:**
@@ -884,10 +884,10 @@ lines(73:84, fc$mean, col = "red", lwd = 2)
 ```{r sales-example}
 # Simulated retail sales: trend + seasonality + promotions
 set.seed(123)
-months <- 1:60
+months <- 1:120
 trend <- 1000 + 20 * months
 seasonal <- 200 * sin(2 * pi * months / 12) # Holiday peaks
-noise <- rnorm(60, sd = 50)
+noise <- rnorm(120, sd = 50)
 data <- trend + seasonal + noise
 
 # Try multiple models

vignettes/forecastbaselines.Rmd

@@ -320,7 +320,7 @@ For data with strong seasonal patterns, use seasonal models:
 ```{r seasonal}
 # Generate seasonal data (monthly, s=12)
 set.seed(789)
-seasonal_data <- 10 + 5 * sin(2 * pi * (1:60) / 12) + rnorm(60)
+seasonal_data <- 10 + 5 * sin(2 * pi * (1:120) / 12) + rnorm(120)
 
 # STL decomposition model
 stl_model <- STLModel(s = 12)